Report

• Number of hidden layers = 1, number of neurons in hidden layer = 10.
• Tested for number of epochs = 100, batch size = 10, training sample size = 6000
• Here I limited to 100 epochs since the around 200 the process was killed (probably due to memory shortage).
• alpha used is :: 0.1
• Optimum Batch size came out to be 10
• The mininum epoch achieved was :: 0.0008333333

Serial Code

• The serial code is neural_net.c
• Time Taken :: 120.14 sec
• Accuracy :: 75 percent
• Grind rate :: 4994.17

Parallel Code

• The parallel code is neural_net_omp.c

• Time Taken :: 30.8 sec

• Accuracy :: 66 percent

• Grind rate :: 19480.106

• Since the major calculation portion was the matrix multiplications and other operations I parallelised all the matrix operations.

• Apart from that I parallelised on places where I am looping through all the layers and not sharing resources for every input.

• It was not possible according to my architecture to parallelize for m for loop since for that every input will have to have separate layer_a(activation layer array), layer_z(Z of layers) etc.

• The speed increased but the accuracy dropped I beleive its due to some race conditions while paralleslising which I could not debug.